Related Experiment Video
Updated: Oct 18, 2026

Reconstructing Terrestrial Paleoclimate and Paleoecology with Fossil Leaves Using Digital Leaf Physiognomy and Leaf Mass Per Area
Published on: October 25, 2024
Surface correlation functions of dead-leaves models
1University of Liège, Department of Chemical Engineering, Liège 4000, Belgium.
Abstract:
The pore-surface and surface-surface correlation functions are structural characteristics that play an important role in theoretical materials science and in small-angle scattering theory. Symbolic expressions for the surface correlation functions are available only for very few models, and we here derive such expressions for the general class of dead-leaves models. Within these models, a two-phase pore-solid structure is created by sequentially and randomly filling space with porelike or solidlike grains that overlap any preexisting structure, in the same way that dead leaves fall on the ground. The obtained mathematical expressions are valid for any grain shape, in arbitrary dimension. The results are illustrated with monodispersed spherical grains as well as with a dead-leaves realization of a Debye random medium. In the latter case, the size distribution of the grains is designed to produce a structure having an exponential two-point correlation function. Compared to Debye random media obtained by numerical reconstruction, the dead-leaves structure has an almost identical surface-surface correlation function, but a distinctly different pore-surface correlation function with different average mean curvature of the pore-solid surface. Using the concepts introduced in the paper, we submit a general analytic expression for the pore-surface and surface-surface correlation functions of the Boolean model of interpenetrating grains, valid for arbitrary grains. We also derive general expressions for the surface correlation functions of mosaic structures.
Related Concept Videos
Residuals and Least-Squares Property
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
Residual Plots
When the residual values are plotted against the variable x, it is called a residual...
Coefficient of Correlation
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
The size of the correlation r indicates the strength of the linear...
Modeling with Differential Equations
Correlation and Regression
Boundary Layer Characteristics

